import pandas as pd
import plotly as py
import plotly.graph_objs as go
from pyecharts.globals import ChartType, SymbolType
from plotly.graph_objs import Scatter, Layout, Data
import pandas as pd
df = pd.read_csv("junfei.csv",encoding="GBK")
df.head()
add =list(df['Region'])
junfei1960=list(df['1960'])
jf1960 = []
for i in junfei1960:
jf1960.append(int(i))
#1960数据
junfei1961=list(df['1961'])
jf1961 = []
for i in junfei1961:
jf1961.append(int(i))
junfei1962=list(df['1962'])
jf1962 = []
for i in junfei1962:
jf1962.append(int(i))
junfei1963=list(df['1963'])
jf1963 = []
for i in junfei1963:
jf1963.append(int(i))
junfei1964=list(df['1964'])
jf1964 = []
for i in junfei1964:
jf1964.append(int(i))
junfei1965=list(df['1965'])
jf1965 = []
for i in junfei1965:
jf1964.append(int(i))
junfei1966=list(df['1966'])
jf1966 = []
for i in junfei1966:
jf1966.append(int(i))
junfei1967=list(df['1967'])
jf1967 = []
for i in junfei1967:
jf1967.append(int(i))
junfei1968=list(df['1968'])
jf1968 = []
for i in junfei1968:
jf1968.append(int(i))
junfei1969=list(df['1969'])
jf1969 = []
for i in junfei1969:
jf1969.append(int(i))
junfei1970=list(df['1970'])
jf1970 = []
for i in junfei1970:
jf1970.append(int(i))
junfei1971=list(df['1971'])
jf1971 = []
for i in junfei1971:
jf1971.append(int(i))
junfei1972=list(df['1972'])
jf1972 = []
for i in junfei1972:
jf1972.append(int(i))
junfei1973=list(df['1973'])
jf1973 = []
for i in junfei1973:
jf1973.append(int(i))
junfei1974=list(df['1974'])
jf1974 = []
for i in junfei1974:
jf1974.append(int(i))
junfei1975=list(df['1975'])
jf1975 = []
for i in junfei1975:
jf1975.append(int(i))
junfei1976=list(df['1976'])
jf1976 = []
for i in junfei1976:
jf1976.append(int(i))
junfei1977=list(df['1977'])
jf1977 = []
for i in junfei1977:
jf1977.append(int(i))
junfei1978=list(df['1978'])
jf1978 = []
for i in junfei1978:
jf1978.append(int(i))
junfei1979=list(df['1979'])
jf1979 = []
for i in junfei1979:
jf1979.append(int(i))
junfei1980=list(df['1980'])
jf1980 = []
for i in junfei1980:
jf1980.append(int(i))
junfei1981=list(df['1981'])
jf1981 = []
for i in junfei1981:
jf1981.append(int(i))
junfei1982=list(df['1982'])
jf1982 = []
for i in junfei1982:
jf1982.append(int(i))
junfei1983=list(df['1983'])
jf1983 = []
for i in junfei1983:
jf1983.append(int(i))
junfei1984=list(df['1984'])
jf1984 = []
for i in junfei1984:
jf1984.append(int(i))
junfei1985=list(df['1985'])
jf1985 = []
for i in junfei1985:
jf1985.append(int(i))
junfei1986=list(df['1986'])
jf1986 = []
for i in junfei1986:
jf1986.append(int(i))
junfei1987=list(df['1987'])
jf1987 = []
for i in junfei1987:
jf1987.append(int(i))
junfei1988=list(df['1988'])
jf1988 = []
for i in junfei1988:
jf1988.append(int(i))
junfei1989=list(df['1989'])
jf1989 = []
for i in junfei1989:
jf1989.append(int(i))
junfei1990=list(df['1990'])
jf1990 = []
for i in junfei1990:
jf1990.append(int(i))
junfei1991=list(df['1991'])
jf1991 = []
for i in junfei1991:
jf1991.append(int(i))
junfei1992=list(df['1992'])
jf1992 = []
for i in junfei1992:
jf1992.append(int(i))
junfei1993=list(df['1993'])
jf1993 = []
for i in junfei1993:
jf1993.append(int(i))
junfei1994=list(df['1994'])
jf1994 = []
for i in junfei1994:
jf1994.append(int(i))
junfei1995=list(df['1995'])
jf1995 = []
for i in junfei1995:
jf1995.append(int(i))
junfei1996=list(df['1996'])
jf1996 = []
for i in junfei1996:
jf1996.append(int(i))
junfei1997=list(df['1997'])
jf1997 = []
for i in junfei1997:
jf1997.append(int(i))
junfei1998=list(df['1998'])
jf1998 = []
for i in junfei1998:
jf1998.append(int(i))
junfei1999=list(df['1999'])
jf1999 = []
for i in junfei1999:
jf1999.append(int(i))
junfei2000=list(df['2000'])
jf2000 = []
for i in junfei2000:
jf2000.append(int(i))
junfei2001=list(df['2001'])
jf2001 = []
for i in junfei2001:
jf2001.append(int(i))
junfei2002=list(df['2002'])
jf2002 = []
for i in junfei2002:
jf2002.append(int(i))
junfei2003=list(df['2003'])
jf2003 = []
for i in junfei2003:
jf2003.append(int(i))
junfei2004=list(df['2004'])
jf2004 = []
for i in junfei2004:
jf2004.append(int(i))
junfei2005=list(df['2005'])
jf2005 = []
for i in junfei2005:
jf2005.append(int(i))
junfei2006=list(df['2006'])
jf2006 = []
for i in junfei2006:
jf2006.append(int(i))
junfei2007=list(df['2007'])
jf2007 = []
for i in junfei2007:
jf2007.append(int(i))
junfei2008=list(df['2008'])
jf2008 = []
for i in junfei2008:
jf2008.append(int(i))
junfei2009=list(df['2009'])
jf2009 = []
for i in junfei2009:
jf2009.append(int(i))
junfei2010=list(df['2010'])
jf2010 = []
for i in junfei2010:
jf2010.append(int(i))
junfei2011=list(df['2011'])
jf2011 = []
for i in junfei2011:
jf2011.append(int(i))
junfei2012=list(df['2012'])
jf2012 = []
for i in junfei2012:
jf2012.append(int(i))
junfei2013=list(df['2013'])
jf2013 = []
for i in junfei2013:
jf2013.append(int(i))
junfei2014=list(df['2014'])
jf2014 = []
for i in junfei2014:
jf2014.append(int(i))
junfei2015=list(df['2015'])
jf2015 = []
for i in junfei2015:
jf2015.append(int(i))
junfei2016=list(df['2016'])
jf2016 = []
for i in junfei2016:
jf2016.append(int(i))
junfei2017=list(df['2017'])
jf2017 = []
for i in junfei2017:
jf2017.append(int(i))
junfei2018=list(df['2018'])
jf2018 = []
for i in junfei2018:
jf2018.append(int(i))
from pyecharts.charts import Map,Timeline
from pyecharts import options as opts
def map_world()->Map:
a = (
Map()
.add("美元",list(zip(add,jf1998)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return a
a = map_world()
def map_world()->Map:
b = (
Map()
.add("美元",list(zip(add,jf1999)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1999年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return b
b = map_world()
def map_world()->Map:
c = (
Map()
.add("美元",list(zip(add,jf2000)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2000年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return c
c = map_world()
def map_world()->Map:
d = (
Map()
.add("美元",list(zip(add,jf2001)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2001年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return d
d = map_world()
def map_world()->Map:
e = (
Map()
.add("美元",list(zip(add,jf2002)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2002年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return e
e = map_world()
def map_world()->Map:
f = (
Map()
.add("美元",list(zip(add,jf2003)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2003年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return f
f = map_world()
def map_world()->Map:
g = (
Map()
.add("美元",list(zip(add,jf2004)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2004年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return g
g = map_world()
def map_world()->Map:
h = (
Map()
.add("美元",list(zip(add,jf2005)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2005年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return h
h = map_world()
def map_world()->Map:
i = (
Map()
.add("美元",list(zip(add,jf2006)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2006年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return i
i = map_world()
def map_world()->Map:
j = (
Map()
.add("美元",list(zip(add,jf2007)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2007年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return j
j = map_world()
def map_world()->Map:
k = (
Map()
.add("美元",list(zip(add,jf2008)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2008年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return k
k = map_world()
def map_world()->Map:
l = (
Map()
.add("美元",list(zip(add,jf2009)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2009年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return l
l = map_world()
def map_world()->Map:
m = (
Map()
.add("美元",list(zip(add,jf2010)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2010年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return m
m = map_world()
def map_world()->Map:
n = (
Map()
.add("美元",list(zip(add,jf2011)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2011年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return n
n = map_world()
def map_world()->Map:
o = (
Map()
.add("美元",list(zip(add,jf2012)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2012年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return o
o = map_world()
def map_world()->Map:
p = (
Map()
.add("美元",list(zip(add,jf2013)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2013年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return p
p = map_world()
def map_world()->Map:
q = (
Map()
.add("美元",list(zip(add,jf2014)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2014年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return q
q = map_world()
def map_world()->Map:
r = (
Map()
.add("美元",list(zip(add,jf2015)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2015年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return r
r = map_world()
def map_world()->Map:
s = (
Map()
.add("美元",list(zip(add,jf2016)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2016年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return s
s = map_world()
def map_world()->Map:
t = (
Map()
.add("美元",list(zip(add,jf2017)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "2017年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return t
t = map_world()
def map_world()->Map:
u = (
Map()
.add("美元",list(zip(add,jf2018)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False),)
.set_global_opts(
title_opts = opts.TitleOpts(title = "2018年世界各地军费"),
visualmap_opts=opts.VisualMapOpts(min_=1,max_=649000000000),
)
)
return u
u = map_world()
timeline = Timeline()
timeline.add(a, '1998')
timeline.add(b, '1999')
timeline.add(c, '2000')
timeline.add(d, '2001')
timeline.add(e, '2002')
timeline.add(f, '2003')
timeline.add(g, '2004')
timeline.add(h, '2005')
timeline.add(i, '2006')
timeline.add(j, '2007')
timeline.add(k, '2008')
timeline.add(l, '2009')
timeline.add(m, '2010')
timeline.add(n, '2011')
timeline.add(o, '2012')
timeline.add(p, '2013')
timeline.add(q, '2014')
timeline.add(r, '2015')
timeline.add(s, '2016')
timeline.add(t, '2017')
timeline.add(u, '2018')
timeline.render_notebook()
dfd = pd.read_csv("junfeizhanbi.csv",encoding="GBK")
dfd.head()
zhanbi1998=list(dfd['1998'])
zb1998 = []
for i in zhanbi1998:
zb1998.append(round(i,2))
zhanbi1999=list(dfd['1999'])
zb1999 = []
for i in zhanbi1999:
zb1999.append(round(i,2))
zhanbi2000=list(dfd['2000'])
zb2000 = []
for i in zhanbi2000:
zb2000.append(round(i,2))
zhanbi2001=list(dfd['2001'])
zb2001 = []
for i in zhanbi2001:
zb2001.append(round(i,2))
zhanbi2002=list(dfd['2002'])
zb2002 = []
for i in zhanbi2002:
zb2002.append(round(i,2))
zhanbi2003=list(dfd['2003'])
zb2003 = []
for i in zhanbi2003:
zb2003.append(round(i,2))
zhanbi2004=list(dfd['2004'])
zb2004 = []
for i in zhanbi2004:
zb2004.append(round(i,2))
zhanbi2005=list(dfd['2005'])
zb2005 = []
for i in zhanbi2005:
zb2005.append(round(i,2))
zhanbi2006=list(dfd['2006'])
zb2006 = []
for i in zhanbi2006:
zb2006.append(round(i,2))
zhanbi2007=list(dfd['2007'])
zb2007 = []
for i in zhanbi2007:
zb2007.append(round(i,2))
zhanbi2008=list(dfd['2008'])
zb2008 = []
for i in zhanbi2008:
zb2008.append(round(i,2))
zhanbi2009=list(dfd['2009'])
zb2009 = []
for i in zhanbi2009:
zb2009.append(round(i,2))
zhanbi2010=list(dfd['2010'])
zb2010 = []
for i in zhanbi2010:
zb2010.append(round(i,2))
zhanbi2011=list(dfd['2011'])
zb2011 = []
for i in zhanbi2011:
zb2011.append(round(i,2))
zhanbi2012=list(dfd['2012'])
zb2012 = []
for i in zhanbi2012:
zb2012.append(round(i,2))
zhanbi2013=list(dfd['2013'])
zb2013 = []
for i in zhanbi2013:
zb2013.append(round(i,2))
zhanbi2014=list(dfd['2014'])
zb2014 = []
for i in zhanbi2014:
zb2014.append(round(i,2))
zhanbi2015=list(dfd['2015'])
zb2015 = []
for i in zhanbi2015:
zb2015.append(round(i,2))
zhanbi2016=list(dfd['2016'])
zb2016 = []
for i in zhanbi2009:
zb2016.append(round(i,2))
zhanbi2017=list(dfd['2017'])
zb2017 = []
for i in zhanbi2017:
zb2017.append(round(i,2))
zhanbi2018=list(dfd['2018'])
zb2018 = []
for i in zhanbi2018:
zb2018.append(round(i,2))
from pyecharts.charts import Map,Timeline
from pyecharts import options as opts
def map_world()->Map:
a = (
Map()
.add("美元",list(zip(add,zb1998)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return a
a = map_world()
def map_world()->Map:
b = (
Map()
.add("美元",list(zip(add,zb1999)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return b
b = map_world()
def map_world()->Map:
c = (
Map()
.add("美元",list(zip(add,zb2000)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return c
c = map_world()
def map_world()->Map:
d = (
Map()
.add("美元",list(zip(add,zb2001)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return d
d = map_world()
def map_world()->Map:
e = (
Map()
.add("美元",list(zip(add,zb2002)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return e
e = map_world()
def map_world()->Map:
f = (
Map()
.add("美元",list(zip(add,zb2003)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return f
f = map_world()
def map_world()->Map:
g = (
Map()
.add("美元",list(zip(add,zb2004)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return g
g = map_world()
def map_world()->Map:
h = (
Map()
.add("美元",list(zip(add,zb2005)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return h
h = map_world()
def map_world()->Map:
i = (
Map()
.add("美元",list(zip(add,zb2006)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return i
i = map_world()
def map_world()->Map:
j = (
Map()
.add("美元",list(zip(add,zb2007)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return j
j = map_world()
def map_world()->Map:
k = (
Map()
.add("美元",list(zip(add,zb2008)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return k
k = map_world()
def map_world()->Map:
l = (
Map()
.add("美元",list(zip(add,zb2009)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return l
l = map_world()
def map_world()->Map:
m = (
Map()
.add("美元",list(zip(add,zb2010)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return m
m = map_world()
def map_world()->Map:
n = (
Map()
.add("美元",list(zip(add,zb2011)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return n
n = map_world()
def map_world()->Map:
o = (
Map()
.add("美元",list(zip(add,zb2012)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return o
o = map_world()
def map_world()->Map:
p = (
Map()
.add("美元",list(zip(add,zb2013)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return p
p = map_world()
def map_world()->Map:
q = (
Map()
.add("美元",list(zip(add,zb2014)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return q
q = map_world()
def map_world()->Map:
r = (
Map()
.add("美元",list(zip(add,zb2015)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return r
r = map_world()
def map_world()->Map:
s = (
Map()
.add("美元",list(zip(add,zb2016)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return s
s = map_world()
def map_world()->Map:
t = (
Map()
.add("美元",list(zip(add,zb2017)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False))
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return t
t = map_world()
def map_world()->Map:
u = (
Map()
.add("美元",list(zip(add,zb2018)),"world")
.set_series_opts(label_opts = opts.LabelOpts(is_show = False),)
.set_global_opts(
title_opts = opts.TitleOpts(title = "1998年世界各地军费占GDP比"),
visualmap_opts=opts.VisualMapOpts(min_=0,max_=4),
)
)
return u
u = map_world()
timeline = Timeline()
timeline.add(a, '1998')
timeline.add(b, '1999')
timeline.add(c, '2000')
timeline.add(d, '2001')
timeline.add(e, '2002')
timeline.add(f, '2003')
timeline.add(g, '2004')
timeline.add(h, '2005')
timeline.add(i, '2006')
timeline.add(j, '2007')
timeline.add(k, '2008')
timeline.add(l, '2009')
timeline.add(m, '2010')
timeline.add(n, '2011')
timeline.add(o, '2012')
timeline.add(p, '2013')
timeline.add(q, '2014')
timeline.add(r, '2015')
timeline.add(s, '2016')
timeline.add(t, '2017')
timeline.add(u, '2018')
timeline.render_notebook()
dfc= df.set_index("Country Name")
dfc.head()
中国 = go.Scatter(
x=[pd.to_datetime('01/01/{y}'.format(y=x), format="%m/%d/%Y") for x in dfc.columns[31:].values],
y=dfc.loc["中国",:][31:].values,
name = "中国"
)
美国 = go.Scatter(
x=[pd.to_datetime('01/01/{y}'.format(y=x), format="%m/%d/%Y") for x in dfc.columns[31:].values],
y=dfc.loc["美国",:][31:].values,
name = "美国"
)
layout = dict(xaxis=dict(rangeselector=dict( buttons=list([
dict(count=3,
label="3年",
step="year",
stepmode="backward"),
dict(count=5,
label="5年",
step="year",
stepmode="backward"),
dict(count=10,
label="10年",
step="year",
stepmode="backward"),
dict(step="all")
])),
rangeslider=dict(bgcolor="#70EC57"),
title='年份'
),
yaxis=dict(title='中美军费增长图'),
title="中美军费增长图"
)
abc = dict(data=[美国,中国], layout=layout)
py.offline.iplot(abc)
dfg = pd.read_csv("wuzhuangrenyuan_zongshu.csv",encoding="GBK")
dff= dfg.set_index("Region")
mgrs=list(dff.iloc[249])[42:]
mgrss=[]
for i in mgrs:
mgrss.append(round(i / 1000000,2))
zgrs = list(dff.iloc[38])[42:]
zgrss=[]
for i in zgrs:
zgrss.append(round(i / 1000000,2))
tmls = list(dff.columns[42:])
dft = pd.read_csv("wuzhuangrenyuan_zhanbi.csv",encoding="GBK")
dfe= dft.set_index("Region")
mgzb = list(dfe.iloc[249])[42:]
mgzbb=[]
for i in mgzb:
mgzbb.append(round(i,2))
zgzb = list(dfe.iloc[38])[42:]
zgzbb=[]
for i in zgzb:
zgzbb.append(round(i,2))
from pyecharts import options as opts
from pyecharts.charts import Bar, Grid, Line,Scatter
def grid_vertical() -> Grid:
bar = (
Bar()
.add_xaxis(tmls)
.add_yaxis("zg",zgrss)
.add_yaxis("mg", mgrss)
.set_global_opts(title_opts=opts.TitleOpts(title="中美军人数量(百万)"))
)
line = (
Line()
.add_xaxis(tmls)
.add_yaxis("zg",zgzbb)
.add_yaxis("mg",mgzbb)
.set_global_opts(
title_opts=opts.TitleOpts(title="中美军人数量与劳动力占比", pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
)
)
grid = (
Grid()
.add(bar, grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(line, grid_opts=opts.GridOpts(pos_top="60%"))
)
return grid
grid_vertical().render_notebook()